G now produce a scatterplot of takeoff velocity vs

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(g) Now produce a scatterplot of takeoff velocity vs. percentage of body fat. Describe the association. Would you say that the association with velocity is stronger than with body mass? More or less linear? (h) For the other two variables (hind limb length and muscle mass), would you expect to see a positive or negative association with takeoff velocity? Explain. Then look at scatterplots, and comment on whether the association is as you expected.
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Chance/Rossman, 2015 ISCAM III Investigation 5.6 357 Technology Detour Coded Scatterplots In R x Create the scatterplot as before, but pass a categorical variable as a color vector. For example: > plot(velocity~bodymass, col=sex) In Minitab x Choose Graph > Scatterplot , with the “With Groups” option. Click OK . x Enter the response and explanatory variables as before. x Click in the Categorical variables box and enter the Sex column (e.g., double click on it in left panel). Press OK . (i) Now produce a coded scatterplot of takeoff velocity vs. body mass that uses different symbols for male and female cats (see Technology Detour above). Based on this graph, do you notice any differences between male and female cats with regard to these variables? Explain. Study Conclusions These researchers reported that variation in cat maximum takeoff velocity was significantly explained by both hind limb length (cats with longer limbs tended to have higher takeoff velocities) and fat mass relative to lean body mass (cats with lower fat mass tended to have higher takeoff velocities), but not to extensor muscle mass relative to lean mass or fast- twitch fiber content. They explained the “pervasive effect” of body mass by the increase in muscle work invested in increasing the center of mass potential energy as compared with kinetic energy during takeoff. Later in this chapter you will learn how they determined the statistical significance of these relationships. First, we will examine a numerical measure of the strength of the association between two variables.
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Chance/Rossman, 2015 ISCAM III Investigation 5.7 358 Investigation 5.7: Drive for Show, Putt for Dough Some have cited “Drive for show, putt for dough” as the oldest cl iché in golf. The message is that the best way to improve one’s scoring average in golf is to focus on improving putting, as opposed to, say, distance off the initial drive, even though the latter usually garners more ooh’s and aah’s . To see whether this philosophy has merit, we need to examine whether there is a relationship between putting ability and overall scoring, and whether that relationship is stronger than the relationship between scoring average and driving distance. The file golfers.txt contains the 2004 statistics (through the Honda Classic on March 20) on the top 80 PGA golfers, downloaded from http://www.pgatour.com/stats/ on March 20, 2004. Three of the variables recorded include: x Scoring average : A weighted average which takes the stroke average of the field into account. It is
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